Nationaal Lucht- en Ruimtevaartlaboratorium National Aerospace Laboratory NLR DXXX-1A AI Planning at...

Nationaal Lucht- en RuimtevaartlaboratoriumNational Aerospace Laboratory NLR

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AI Planning at airports:Departure Management

PLANET Industry Day

Charles University

Prague

26 May 2003

H.H. (Henk) HesselinkGroup leader AI and Airport Decision Support Systems

([email protected])


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Today

Introduction NLR

Planning at airports

Departure management - how can AI planning help?

Departure sequencing - details

Results so far and future


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National Aerospace Laboratory (NLR)

NLR is a non-profit foundation since 1937/1919

NLR provides technical and scientific contributions to activities in aerospace related areas

NLR will independently serve public and private organisations

NLR

Education– Universities– Technical Colleges

GovernmentMinistries– Transport

– Defence– Environment

Operators

Industrys

– Aircraft– Space– Other, incl.

Electronics,Remote Sensing

– Royal NL Air Force– Royal NL Navy– KLM– Air Traffic Control– Amsterdam Airport

Schiphol


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Division of Activities into Categories Supported

Civil aerospace: 65% - Military aerospace: 35%

Aeronautics: 85% - Space: 15%

Operations: 60% - Development: 40%

(non-aerospace: < 2%)

Turn over: 150 million hfl.


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Facilities Large Wind Tunnels (2 low speed, 2 transonic,

1 supersonic, 50% shared in DNW) Simulators (flight, air traffic control, tower)

Aircraft (Fairchild Metro II, Cessna Citation II)

environments (supercomputer, network, middleware)


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Planning at airportsWhy do we need planning and what is the most

promising step at this moment: departure management


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Planning, the basis

Planning has been the subject of many projects (a.o. military)

Planning in en-route (during flight) is examined– Free routing / autonomous pilot is a concept where the pilot flies

his route without intervening with air traffic control - this cannot work without planning

Planning for arrival traffic (still flying) is being implemented at several airports now– Arrival management

=> Planning at airports is a next step


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5

1997 2000

2010 2020

DIVISION DED 4 - 4/11/97

7.0 Mio Flights 8.0 Mio Flights

11.9 Mio Flights 15.8 Mio Flights

Flights 150 or more

Flights 100 to 150

Flights 50 to 100

Traffic Growth

ATM Planning: the Problem


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Each airport is different but the same


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Planning decisions to be taken ...

Apron start upTaxiway crossing

Runway holdingIntersection take-off

Deicing area

Left or right track


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Current airport planners

Airport planner 1: pre-flight controller

Responsible for giving clearances and information before the aircraft actually starts moving

Gives SID (= departure route)

Co-ordinates with CFMU (European co-ordinated slot time, central agency in Brussels)

Main problem: communication overload


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Airport planner - 2: ground controller

Responsible for movements over the taxiways of the airport

Gives taxiing routes

Merges inbound and outbound traffic

Establishes departure sequences

Assesses runway and/or apron congestion to avoid taxiway congestion (use of holdings and parkings)


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Airport planner - 3: tower controller

Responsible for traffic at runways

Usually one runway per tower controller

Segregated mode vs. mixed mode operations

Large aircraft separation necessary because of wake vortex and speed differences


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Airport planner - 4: approach controller

Responsible for the airspace around the airport

Sequences arrival traffic

Uses STARs (Standard Arrival Routes) and stacks

Takes care of dependencies between runways (e.g. crossing runways or converging runways), always aware of overshoots or missed approaches

Determines the runway capacity


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Airport planner - 5: supervisor

Responsible for the flow of traffic

Decides on runway usage

Assesses meteo and decides on airport acceptance rate (per hour) - information will be send to Brussels (CFMU)

Co-ordinates work of other controllers, assesses their workload and decides on division of work over 1 to 10 controllers


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Departure Management

How can AI Planning Help?


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Departure sequencing: model

SID way point

SID A start point

Runway 1holding

Runway2

Runway 2entry A

Runway 1entry A

Runway1

Runway 2holding

SID B start point

TMA exit point A

TMA exit point B

TaxiwaystructureApron

structure

Remaining SIDs

Remaining SIDs

Bottleneck


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Departure Sequencing Problem

Sequencing aircraft at the available runways

Computer assistance to the controller team in the control tower

Co-operate with other planners

Planning will be performed before the aircraft starts moving (20 - 30 minutes in advance). Tool provides sequences on an “optimal” basis, in stead of (smart) first-come-first-served


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What variables must be planned?

runway

SID

structure

2 min.

5 min.

exit

holding

A

* runway assignment

runway

holding

A2 min.

5 min.

exit

* intersection take-off* take-off time (sequences)* SID allocation


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NLR departure sequencer

The details of constraint reasoning


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What is planning

Planning is the preparation of actions (movements) in the broadest sense of the word– setting a goal– determining actions to achieve this goal– sequencing these actions

It takes into account the current situation, available resources, changing information, …

Many constraints


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Constraints

Some constraints are hard (CFMU, wake vortex, ..)

Some constraints are soft (distribution, pilot wishes, ..)

Constraints differ per airport

Constraints differ per “situation”

=> We need an implementation that follows all rules (regulations) but still provides some flexibility

=> AI Planning: constraint satisfaction with optimisation


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Constraint Satisfaction

S E N D

M O R E +

M O N E Y

S = [0,1,2,3,4,5,6,7,8,9]

E = [0,1,2,3,4,5,6,7,8,9]

N = [0,1,2,3,4,5,6,7,8,9]

D = [0,1,2,3,4,5,6,7,8,9]

M = [0,1,2,3,4,5,6,7,8,9]

O = [0,1,2,3,4,5,6,7,8,9]

R = [0,1,2,3,4,5,6,7,8,9]

Y = [0,1,2,3,4,5,6,7,8,9]

C1 C2 C3 C4

C = [0, 1]

_ _________________

--

--

--

--

--

--

--C2 + S + M > 10

etc...


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Departure seq.: constraint satisfaction

Departure slots can be represented asF1runway = [16L, 16R, 34L, 34R, 07, 25]F1time = [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]F1SID = [ELBA5A, ELBA5B, ELBA5C]

F2runway = [16L, 16R, 34L, 34R, 07, 25]F2time = [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]F2SID = [ELBA5A, ELBA5B, ELBA5C]

Constraints: e.g. 3 min. separation between heavy - light:For F1 <> F2 and Runway (F1 = F2) and (Time(F1) < Time (F2)) and (Weight (F1) > Weight(F2))Conclude Time(F1) + 3 <= Time (F2)


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Runway assignment– Environmental constraints– Meteo conditions– Different runway entry points (CAVOK)

Separation– Wake vortex– Speed

Distribution– Initial climb routes/SIDs– TMA exit point acceptance rate


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Time– Achieve CFMU time constraints (flow restrictions)– Give preference to “late” aircraft

Optimisation– Minimise runway throughput– Make efficient use of available airspace– Depart as early as possible– Provide flexibility

Controller/pilot– Controller makes decisions, machine supports– Pilot may have preferences


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Departure sequencing: HMIs

-5 0 +5 +10 +15

+20

UTC

eobtetd

CALLSIGN STND CTOT EOBT TYPE/W DEST RWY SID SSR ALERT TAXIROUTE REMARK STAT CLR

TVS338 N26 1947 B734/M LEPA 24 BANAS2A 0234

BCS916 N5 1945 1940 B722/M EDDF 24 KADNO 0237

TAR8861 N22 1940 1928 B732/M DTTA 31 KADNO 0344

TAR229 N27 1930 1924 A30B/M DTTA 31 RAK 0332

CSA978 N20 1910 1904 AT72/M LZKZ 24 RATIS 0336 J-H-B

FFR8105 N1 1920 1915 B733/M LDDU 24 BANAS2A 0232

CSA270 N9 1916 1909 B735/M HECA 24 RATIS 0331 NO PUSH

Active Departures

Delete Edit TWR/APP

-5 0 +5 +15+10


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Trial implementation at Prague airport (Integrated in the NOVA system)

-5 0 +5 +10 +15 +20


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Results so far and future work

-5 0 +5 +10 +15 +20

Where do we stand and what do we expect from the future


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Departure sequencing: concept

Departure sequence

Intersection take-off

Taxiway routing

Co-ordination

Apron management

Co-ordination

Start-up

Push back

De-icingRunway allocation

Wake vortex

Speed

Flow restrictions

CFMU slots

SID usage


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Controller support in planning

With the provision of a “ departure management” function, we will be able to:

Enable a better knowledge of the current and future situation (situation awareness)

Provide continuous optimal capacity

Provide the controller a new challenging operational task– make strategic decisions on departure management– provide advanced guidance– increase safety with new complex procedural demands


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Results so far Implementation in C++, connected to CORBA in A-SMGCS

simulator; core scheduler build in ILOG

Off-line DMAN evaluations at Rome-Fiumicino and Paris-Orly (1998-1999)

On-line DMAN tests at Prague and Hamburg (2001-2002)

Simulator trials for Frankfurt (2002)(Tower simulator)

Current activities:– Connection to pilot (CDM)– New trials with controllers in the simulator


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Results so far

In a new set up, we are investigating planning & negotiation algorithms: intelligent agents

Several contributions to “Advanced Airport Technology” course at the Institute of Air Navigation Services (Eurocontrol Luxembourg)– Departure management course– NLR Demonstration is running at IANS

Several papers have been published


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Planning and co-ordination

ActorAgent

Interface

Planner Agent

Planner Agent PlannerInterface

PlannerInterface

ActorAgent

Interface

Blackboard

Blackboardmanager

TARGET SETTING

ACTING


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Some results from operational trials

Prague evaluations (27 May - 7 June 2002)Hamburg evaluations (2 - 13 September 2002)

DMAN resulted in a smoother traffic flow for runway occupancy (results are obtained from simulator trials)

Training Session 2 Evaluation 1 (DMAN)Tower Controller no.2

0:00:00

0:01:26

0:02:53

0:04:19

0:05:46

1 2 3 4 5 6 7 8 9 10

Aircraft Number

Sepa

ratio

n Ti

me

(min

s an

d se

cs)

Training Session 2 Evaluation 2 (no DMAN)Tower Controller no.1

0:00:00

0:01:26

0:02:53

0:04:19

0:05:46

1 2 3 4 5 6 7 8 9 10

Aircraft Number

Sepa

ratio

n Ti

me

(min

s an

d se

cs)

DMAN No DMAN


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Future of airport planning

Planning is a novel concept to be supported

“Forces” controllers to evaluate start-up and push-back requests and gives additional information (situational awareness)

First efficiency benefit have been proven. New demands will arise for e.g. environmental monitoring, in which planning can provide great help

Changing operational procedures is not always appreciated; we slightly started to explore the possibility

Controller wants to be in command


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The result:

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Nationaal Lucht- en Ruimtevaartlaboratorium National Aerospace Laboratory NLR DXXX-1A AI Planning at...

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